Getting started with R/plantbreeding

How to install plantbreeding: 

The "plantbreeding" is written in R language and implemented as an add-on package.

Download R: 

R can be downloaded from The Comprehensive R Archive Network:

http://cran.us.r-project.org/

Both command line and graphical user interface implementation are available.

Download plantbreeding:

The "plantbreeding" is available for in R implementing environments - Microsoft Windows, Linux and Mac OSX operating systems. The software package version “plantbreeding 1.1.0 " is available for free download and use under General Public License from r-forge website (https://r-forge.r-project.org/projects/plantbreeding). The licensing, warranty and distribution policy are same as of R version 2.15.1. 

# install from R-Forge

install.packages("plantbreeding", repos="http://R-Forge.R-project.org")

Once package is installed, implementation is similar to other R software packages. 

Current version of The "plantbreeding" version 1.1.0 compilation consists of altogether 58 functions and datasets. The software will be extended to more functions useful for plant breeding and genetics. The most of functions are complete in themselves returning output in forms different data objects and / or graphs. The objects can piped to other functions available in the R library. As other typical R functions, these functions can be used to build up, user-defined, high level functions. They can be employed in combination with the data analysis routines available in R functions library including base distributions including R/base, R/stats and R/graphics or any other compatible add-in packages.

Detailed manual of each of the function is available on comes with the package and html help can be invoked with:

  help(plantbreeding)

or simply:

?plantbreeding 

Similarly help on any of the functions available can also invoked by: help(name.of.function)

or simply:

? name.of.function

 For example to seek help on carolina1 function, use help(carolina1) or simply ?carolina1.  

Examples of implementation : 

# Diallel analysis 

data(fulldial) 

 out <-diallele1(dataframe = fulldial, male = "MALE", female = "FEMALE",  progeny = "TRT", replication = "REP", yvar = "YIELD" )

print(out) 

out$anvout # analysis of variance 

out$anova.mod1 # analysis of variance for GCA and SCA effects 

out$components.model1 # model1 GCA, SCA and reciprocal components 

out$gca.effmat # GCA effects

out$sca.effmat # SCA effect matrix 

out$reciprocal.effmat # reciprocal effect matrix 

# Analysis of Augmented randomized block design 
data(augblock)
out <- aug.rcb(dataframe = augblock, genotypes = "var", block = "blk", yvar = "gw")
out$anova  # analysis of variance 
out$adjusted_values # yield observed and expected value table